The LTE network consists of evolved NodeBs (eNBs) of an evolved UTRAN (E-UTRAN) and an evolved packet core (EPC), and is a flat network. In the above, the E-UTRAN includes a set of eNBs which are connected to the EPC via S1 interfaces and can connect to each other via X2 interfaces, wherein the S1 and X2 are logical interfaces. One EPC can manage one or more eNBs, one eNB can be controlled by multiple EPCs; and one eNB can manage one or more cells. The LTE-A system is evolved from the LTE system and the network architecture of which is consistent with that of the LTE. Meanwhile, an operation, administration and management (OAM) system, which can perform operations and management to one or more network elements in the network architecture, exists in the network architecture.
On one hand, much works need to be done to establish and operate a network, such as planning, configuration, optimization, computation, adjustment, testing, prevention of errors, failures reduction and self-recovery, thus what the operators mainly concern is how to lower operation and maintenance costs. On the other hand, subscribers require simplifying the process of using, for example, when buying a Home NodeB apparatus, the subscriber wishes a plug-and-play apparatus that can automatically obtain configuration for operation once electrified. The trend of the next generation mobile network is certainly self-configuration, self-optimization, and self-adapting with fewer and fewer human factors impacting on the network. Therefore a concept of self-organizing network came into being, which includes self-configuration and self-optimization. The self-optimization includes coverage and mobility optimizations, wherein the coverage optimization relates to the detections for coverage holes and coverage insufficiencies. The mobility optimization needs to search and analyze on information of various switching events in current cell, wherein the location information of the terminal at the time of events happening plays an important part in analysis of above optimization scenes.
Traditional locating methods include the followings: CELL-ID, CELL-ID+ Round Trip Time (RTT), and Observed Time Difference of Arrival (OTDOA), wherein the CELL-ID locating technique is a most basic locating method, in which the location of the subscriber is determined according to the ID number of the cell where a mobile station locates. The precision of the CELL-ID locating technique depends on cell radius, i.e., the size of the cell where the mobile station locates, ranging from a few hundreds of meters to several tens of kilometers. The locating precision for CELL-ID in the case of suburbs and countryside is relatively low due to the wide coverage of the cell. The cell coverage of urban area is smaller, the cell radius generally being 1-2 km. For a dense urban area where micro cell is applied, the cell radius can be a few hundreds of meters, and the locating precision of CELL-ID is correspondingly improved to a few hundreds of meters. Compared with other techniques, the locating precision of CELL-ID is the lowest. When an emergency locating service with relatively high precision is needed, the CELL-ID can not meet this demand. On the other hand, a locating measurement by the mobile station is unnecessary for the CELL-ID locating, and very little locating signaling needs to be transmitted through an air interface, thus the locating response time is relatively short, usually within 3 seconds.
The CELL-ID+ RTT is an improved technique based on the CELL-ID locating technique, and performs locating by employing the CELL-ID of the current serving cell of the terminal and current RTT information from the terminal to the base station.
The OTDOA is similar to enhanced Observed Time Difference (E-OTD) of a GSM network, in which downlink pilot signals of different base stations are measured through a mobile station, to obtain the Time Of Arrivals (TOAs) of the downlink pilot signals of the different base stations, the location of the mobile station is then figured out by using a proper location estimation algorithm according to the measurement results and the coordinates of the base station.
During network implementation, the terminal is user equipment which directly influences user experience, therefore, in terminal locating, how to make the best use of base station information for locating is helpful to reduce terminal power consumption and improve satisfaction degree of user experience. When the user terminal is at connection status, how to make the best use of the information which can be acquired by the base station to realize terminal locating is what the present invention needs to address.